Current security sensors, which are used to secure a perimeter or volumetric area of a site, include volumetric, linear, video analytic, or curtain style security sensors. These security sensors are prone to high false alarm and nuisance alarm rates due to a variety of environmental and natural triggers. Examples of such triggers include movements of small or large animals, wind blown debris, moving vegetation, temperature gradients, moving clouds, rain, snow, and moving water.
In addition, conventional security sensors are not able to provide geo-locations of targets to aid in moving cameras or displaying target positions on geo-referenced maps or displays, or provide position and direction of travel data. While such security sensors are able to reduce sensitivities to smaller targets or environmental conditions, this is at the expense of reducing the ability to detect actual intrusions. Due to limitations in range or field of view, multiple sensors must be used to cover large sites, with expensive material, installation, reliability, and maintenance costs.
An additional problem of conventional security sensors is the inability to accurately discriminate between areas that are required to be secure and adjacent areas that are not secure. Such discrimination would be useful in cases where a temporary secure area is needed and it is impractical to set up effective physical barriers such as fencing, walls, etc. Instead, the secured area might be marked only by crime scene tape or temporary barricades, for example. In these cases there may be a lot of human or vehicle traffic (e.g., gawkers) outside the secure area.
Accordingly, there is a need for improved security sensors that overcome the above deficiencies.